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Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments

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Abstract

This paper proposes a novel object tracking and recognition system according to reliability assessment of learning in mobile environments. The proposed method is based on marker-less tracking, and the proposed system is composed of two main modules (detection and tracking). The detection module identifies an object to be matched on the current frame corresponding to the database, and then generates the standard object information that has the best reliability of learning. This module is able to re-detect the object when the object is missing in the frame. The tracking module traces the object of interest based on the extracted object information using feature points, descriptors, sub-window of object, and its histogram. Additionally, this module tries to select the reliable object between tracked object by Camshift and tracked object by Optical flow. Experimental results show that the proposed method is more robust than the traditional methods, and that it is able to recognize and track objects of interest with reliability assessment of learning for use with mobile environments.

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Acknowledgments

This work was supported by Dankook University project 2013 for funding.

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Authors and Affiliations

  1. Department of Electronic Engineering, Dankook University, Cheonan, South Korea

    Youngseop Kim & Woori Han

  2. Department of Smart Mobile, Far East University, Eumseong County, South Korea

    Yong-Hwan Lee

  3. Department of Computer Science, Namseoul University, Cheonan, South Korea

    Cheong Ghil Kim

  4. Department of Convergence Security, Kyonggi University, Suwon-si, South Korea

    Kuinam J. Kim

Authors
  1. Youngseop Kim
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  2. Woori Han
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  3. Yong-Hwan Lee
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  4. Cheong Ghil Kim
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  5. Kuinam J. Kim
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Correspondence to Yong-Hwan Lee.

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Kim, Y., Han, W., Lee, YH. et al. Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments. Wireless Pers Commun 94, 267–282 (2017). https://doi.org/10.1007/s11277-016-3292-y

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  • DOI: https://doi.org/10.1007/s11277-016-3292-y

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